Open Access

REVIEW

Economic Power Dispatching from Distributed Generations: Review of Optimization Techniques

Paramjeet Kaur¹, Krishna Teerth Chaturvedi¹, Mohan Lal Kolhe^2,*

1 Department of Electrical & Electronics Engineering, University Institute of Technology, Bhopal, India
2 Faculty of Engineering and Science, University of Agder, Kristiansand, Norway

* Corresponding Author: Mohan Lal Kolhe. Email:

Energy Engineering 2024, 121(3), 557-579. https://doi.org/10.32604/ee.2024.043159

Received 23 June 2023; Accepted 28 September 2023; Issue published 27 February 2024

Abstract

In the increasingly decentralized energy environment, economical power dispatching from distributed generations (DGs) is crucial to minimizing operating costs, optimizing resource utilization, and guaranteeing a consistent and sustainable supply of electricity. A comprehensive review of optimization techniques for economic power dispatching from distributed generations is imperative to identify the most effective strategies for minimizing operational costs while maintaining grid stability and sustainability. The choice of optimization technique for economic power dispatching from DGs depends on a number of factors, such as the size and complexity of the power system, the availability of computational resources, and the specific requirements of the application. Optimization techniques for economic power dispatching from distributed generations (DGs) can be classified into two main categories: (i) Classical optimization techniques, (ii) Heuristic optimization techniques. In classical optimization techniques, the linear programming (LP) model is one of the most popular optimization methods. Utilizing the LP model, power demand and network constraints are met while minimizing the overall cost of generating electricity from DGs. This approach is efficient in determining the best DGs dispatch and is capable of handling challenging optimization issues in the large-scale system including renewables. The quadratic programming (QP) model, a classical optimization technique, is a further popular optimization method, to consider non-linearity. The QP model can take into account the quadratic cost of energy production, with consideration constraints like network capacity, voltage, and frequency. The metaheuristic optimization techniques are also used for economic power dispatching from DGs, which include genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO). Also, Some researchers are developing hybrid optimization techniques that combine elements of classical and heuristic optimization techniques with the incorporation of droop control, predictive control, and fuzzy-based methods. These methods can deal with large-scale systems with many objectives and non-linear, non-convex optimization issues. The most popular approaches are the LP and QP models, while more difficult problems are handled using metaheuristic optimization techniques. In summary, in order to increase efficiency, reduce costs, and ensure a consistent supply of electricity, optimization techniques are essential tools used in economic power dispatching from DGs.

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